Data from: When correcting for regression to the mean is worse than no correction at all

This repository contains the source code and data for our study on the
statistical pitfalls of correcting for Regression to the Mean (RTM). In
biological research, observed changes between initial and final
measurements are often negatively correlated with initial values. While
researchers frequently apply statistical corrections to remove this
artifact, we demonstrate through a structural modeling framework that
these corrections can introduce more bias than they remove if the
underlying causal model is not properly specified. Using simulations of
blood pressure systems and empirical analyses of lizard heat tolerance and
bird telomere attrition, we show that standard adjustments (e.g., Berry et
al. 1984) can create spurious biological trends. We conclude that valid
RTM correction requires an explicit causal model—specifically,
distinguishing between stable between-subject variance and transient
measurement or biological noise—rather than the application of generic
statistical formulas.